1.1 Syntax highlighting

Here is a sample code chunk, just to show that syntax highlighting works as expected.

not_null <- function (v) {
  if (!is.null(v)) return(paste(v, "not null"))
}

iris %>%
  filter(Species == "setosa") %>%
  head()

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

1.2 Verbatim

Here is the structure of the iris dataset.

str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

1.3 Mathjax

A little math formula :

\[x = \sum_{i=1}^{n} \sqrt{\frac{y}{i}} \]

3.1 Base graphics

A base graphics histogram:

hist(rnorm(100))

Figure 3.1: A Histogram made with the basic graphics package

3.2 ggplot2

Same with ggplot2 :

d <- data.frame(x=rnorm(100))
ggplot(data=d) + geom_histogram(aes(x=x), bins = 30)

Figure 3.2: A ggplot2 histogram